Because of its strong representational capacity, a myriad of deep learning-based HDR deghosting methods have emerged in recent years. Kalantari et al. [1]} introduced the first multi-frame HDR imaging method for dynamic scenes based on deep learning. This method feeds the input LDR images into the fusion network after aligning them by optical flow. Wu et al. [2]} used homography to globally align the LDR inputs and then fed them into a UNet-based network for local alignment and fusion. Yan et al. [3]} used a attention mechanism to suppress the non-aligned regions between the input LDR images. Niu et al. [4]} proposed the first GAN-based method for HDR deghosting.
